Fuzzy logic based system and method for information processing with uncertain input data

ABSTRACT

A fuzzy logic information processing system and method are disclosed that may be used not only with known or definite data input but also with uncertain data input. The uncertain data input may be represented by a set of values wherein the possibility of any particular or specific value within the set being the true or accurate value is uncertain. The preferred embodiment of the system provides for an extensor to extend or map a representation of the uncertain data into at least one additional dimension related to dimensions of a set of rules used for making fuzzy logic inferences. The set of rules may be provided effectively in a mapped or graphed form. The set of rules and uncertain data are combined, for instance by locating intersection regions, to produce an output set that may be also be described as a map or plot. In a presently preferred embodiment the uncertain inputs and rules are represented mathematically or symbolically and then operated on to produce an output set. A projector then projects the output set to the desired output dimension as an output for the system. The system output may then be used for control purposes such as, for example only, a combat control system to provide a tactical picture, decision aid, presets for a guidance system, or the like.

STATEMENT OF THE GOVERNMENT INTEREST

The invention described herein may be manufactured and used by or forthe Government of the United States of America for Governmental purposeswithout the payment of any royalties thereon or therefore.

BACKGROUND OF THE INVENTION

(1) Field of the Invention

The present invention relates generally to data integration and decisionsupport systems based on fuzzy logic and, more specifically, to a fuzzylogic based system and method for information processing that is capableof handling uncertainty in the input data.

(2) Description of the Prior Art

Combat system information processing entails the integration of datafrom diverse sources for tactical picture generation and maintenance,situation assessment and planning, and allocation/control of resources.Current methods for data integration and decision support in submarinecombat systems do not adequately account for uncertainty in the inputdata in an automated fashion. Instead they rely heavily on operatormanipulation and human interpretation. On the other hand, in recentyears the amount and flow rate of input data for integration has beenrapidly increasing. It is anticipated that advances in sensor technologywill continue to offer more possibilities in gathering both acoustic andnon-acoustic data from organic as well as off-board sources,environmental and kinematic monitors, and intelligence reports. Thecombat system of the future therefore requires the ability toautomatically manage uncertainty in the input data. Automated methodsfor handling uncertainty in the input data remains an outstandingtechnical issue and constitutes a significant Navy problem as well as ascientific and industrial challenge.

Uncertainty refers to being in a condition of doubt. This is contrastedto a condition of certainty or being definite, known, or specific. In aninformation processing context, uncertainty can be thought of as havinga lack of definitive knowledge necessary to describe the process.Uncertainty in the input may result due to many causes including butcertainly not limited to measurement noise, gaps in sensor information,sensor bias, inadequate number or placement of sensors, transmissionnoise or limitations, and the like. While most signals are measuredwithin a tolerance, e.g., ten volts plus or minus one hundred microvolts, an uncertain signal is not known within the normal tolerances andmay be so uncertain that normally used sensor tolerances aremeaningless. Thus, while a tolerance of one hundred micro volts might bean accepted tolerance for an accurate signal in a particularapplication, an uncertain signal might vary by several volts or by morethan one thousand times the normal accepted tolerance for the signal,thus making the signal quite uncertain in a particular application.Thus, a known or definite signal might be ten volts, an uncertain signalmight be representable only as a possible value between eight and twelvevolts. As another example of uncertain input, a sonar system working ina multipath environment may send out a sonar pulse and receive two orthree sonar pulses in return. All three sonar pulses may be receivedwithin a time frame that would present reasonable distance/directioninformation for receipt from the intended target. Therefore, there isuncertainty associated with the acoustic propagathon path for eachreturned sonar pulse. As another example, it may be possible to get anapproximate targeting solution value immediately since a decision foraction may need to be taken now, whereas in time a more precise valuewill be available. This situation arises in a target motion analysiswhere a fundamental property of bearings-only target motion analysis isthat contact range is not observable for a single-leg ownship motion(wherein a leg is defined as a time interval of constant platformvelocity). The range becomes observable only after an ownship maneuverfollowed by a second leg of motion that therefore introduces atime-latency in the estimation process owing to the necessity ofcollecting sufficient data on all legs of motion. Thus, there are manydifferent scenarios of types of uncertainty that will depend on eachdifferent situation.

As a general matter, an information processing system such as a combatcontrol system or other typical control system will produce one or morespecific or definite control signals in response to the input data. Arepresentative example might include a tactical picture display thatmight show a submarine in relation to other targets. Another examplemight include a control for a motor to adjust rudder position. This isalso true of a fuzzy logic-based control system. Fuzzy logic controlsystems have been employed successfully employed in variousapplications. Moreover, fuzzy logic controllers have been successfullyapplied and demonstrated in underwater combat control systems such as,for example, a conditioned fuzzy logic controller for an acousticvehicle intercept guidance system.

A prior art fuzzy inference system has three basic components. Thefuzzifier converts discrete or crisp input numbers to fuzzy logicmembership values that describe a qualitative description of thediscrete input in semantic terms. For instance, a numerical sensor valuesuch as might be produced from a sensor voltage might be converted fromits discrete, known, or specific values to a fuzzy logic membershipvalue in a qualitative class, e.g., low, medium, or high. The output ofthe fuzzifier is represented in these membership values, and comprisesthe fuzzy input membership values. The fuzzifier is not designed tohandle an input that is inexact and has a possibility of varyingthroughout a range of values.

The input membership values are used by an inference engine. Theinference engine employs a knowledge base of rules that permit one ormore inferences, and subsequent aggregation of all the output membershipfunctions from the rules that are triggered by the fuzzy inputmembership values. Thus, the inference engine maps the fuzzy inputmembership values to a single fuzzy output set based on applicable rulesfrom the knowledge base.

The defuzzifier converts the fuzzy output set to a crisp, discrete,particular output value for subsequent usage, e.g., the controlleroutput in a feedback system. The crisp output is representative of thefuzzy output set and might be analogous to the expected value in aprobability distribution.

In summary, a conventional fuzzy system does not have the mechanism tohandle an uncertain input, yet such inputs are typically encountered inpractice, e.g., data integration for submarine combat control. Simplytaking an average, making an estimate, or calculating a normal value andusing the discrete value so determined as input to the fuzzy logicinference system will limit the information that is available about theuncertainties, and thereby reduce the likelihood of making the bestpossible decision. Consequently, there remains a need for a fuzzylogic-based information processing system that can handle uncertaininput. Those skilled in the art will appreciate the present inventionthat addresses the above and other problems.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide animproved system and method for processing data using fuzzy logic.

It is another object of the present invention to use a fuzzy logicsystem that is capable of processing either certain or uncertain data.

It is yet another object of the present invention to provide an improvedcontrol system and method.

It is yet another object of the present invention to provide an improvedtactical picture and decision support system.

These and other objects, features, and advantages of the presentinvention will become apparent from the drawings, the descriptions givenherein, and the appended claims.

In accordance with the present invention, a fuzzy logic system forprocessing information including uncertain information is disclosed. Atleast one input provides information that is indicative of one or morephysical phenomena. The input is representable by an input set thatdescribes a range of possible values related to the one or more physicalphenomena. When a precise value for the input is not available such thatthe input value is uncertain, then input set is representablemathematically by a first map of possible values related to the one ormore physical phenomena.

The fuzzy logic system comprises an extensor for operating on the inputset to produce an operated set of values that may be represented by anextension of the first map using what might be called an extensor orcylindrical extensor. An inference engine for the fuzzy logic systemincludes a plurality of rules related to the one or more physicalphenomena. The inference engine is operable for manipulating theoperated set of values using the plurality rules for producing aconditioned set of values. A projector or retro-projector is providedfor producing an output signal from the conditioned set: of values. Theoutput signal may be used for various purposes such as in displaying apicture such as a tactical picture or providing other decision supportassistance. The output signal could also be used in a control systemsuch as a guidance and navigation control system, or the likes.

In one embodiment of the invention, the rules may be representedmathematically by a second map. The conditioned set may be representedas a third map that is effectively formed by an intersection of theextended first map and the second map. The projector produces a signalthat may be represented as a mapping with one dimension of the third mapbeing collapsed and provided such that the output signal is in thedesired terms.

A method is provided for a control system such as for a combat controlsystem for utilizing uncertain input data that has an uncertain valuethat is contained within a range of possible values. The uncertain inputdata is representative of physical phenomena and may be derived frommultiple different physical phenomena or may be representative of someparticular phenomena. For instance, an input representative of aparticular target or platform such as a ship or submarine may be derivedfrom information such as sonar signals that indicate the frequency ofpropeller rotation and/or the number of propellers detected and soforth. The information may be inconclusive about what type of platformis detected but may provide a range of possibilities as discussed inmore detail subsequently.

The uncertain input data is presented for use in a fuzzy logic systemwith the representation providing a set of possible values for the inputdata without designating a specific value for the uncertain input data.Rules are provided for the fuzzy logic system in terms related to theuncertain input data such as giving possibilities of the likelihood ofdiesel or nuclear operation and the associated speeds thereof. An outputis then inferred from the fuzzy logic system using the set of possiblevalues for the input data and the rules. The uncertain input data may bedescribed by mathematical values although it is possible also thatlinguistic rules and/or symbols and/or other types of logic could beused. Similarly the rules for the fuzzy logic system may be representedin terms that may be described by a second set of mathematical valuesalthough, as discussed above, it is possible that other types ofrepresentations could be made. A third set of mathematical values isproduced based on the first set and the second set. In a presentlypreferred embodiment, the third set effectively comprises anintersection of the first set and the second set. The output from thefuzzy logic system is then used for various purposes such as forproviding a tactical picture.

The fuzzy logic system of the present invention could be used in acontrol system, such as a combat control system, that comprises at leastone sensor for producing a sensor signal used to produce inputinformation for the control system. The fuzzy logic inference system ofthe control system is operable for receiving the input information whenthe input information is precise and also when the input information isuncertain such that a representation of the information is produced whenthe input information is precise and also when the information isuncertain. Thus, the system can handle either uncertain or certaininformation. When the input information is uncertain, the representationis descriptive of a range of possible values for the input informationand the fuzzy logic inference system is operable for comparing therepresentation to a set of rules to produce an output control signal. Anextensor or cylindrical extensor is used for operating on the inputinformation to produce a first map by introducing at least oneadditional dimension to the input information. The at least oneadditional dimension is related to the set of rules. It will be notedthat the present invention may be used with many dimensions such thatthe steps of operation may not always be easily visualized. The rulesare represented by a second map that includes the at least oneadditional dimension. The fuzzy logic inference system is operable forcomparing the first map to the second map to produce a third map Aprojector or retro projector may be used for operating oil the third mapto form a projection onto a dimension for the output control signal.

The output control signal, as yet another example, may be used in adecision support system to provide a range of possibilities for aidingthe decision maker.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention and many of the attendantadvantages thereto will be readily appreciated as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in conjunction with the accompanying drawings whereincorresponding reference characters indicate corresponding partsthroughout several views of the drawings and wherein:

FIG. 1 is a block diagram of a fuzzy logic information processing systemin accord with the present invention;

FIG. 2 is a block diagram of a fuzzy logic inference system as might beused in the fuzzy logic information processing system of FIG. 1;

FIG. 3 is a graphical representation of the process of mapping anuncertain input through a fuzzy logic inference system to the desiredoutput.

FIG. 4 is a map of rules for a particular illustrative inference systemin accord with the present invention;

FIG. 5 is an arbitrary graph descriptive of a bell or Gaussian curvethat shows a set of possible values for the input for which anyparticular value in the set is uncertain;

FIG. 6 is a map of the uncertain data of FIG. 5 that has been extendedinto another dimension by an extensor or cylindrical extensor in accordwith the present invention:

FIG. 7 is a map of the combination of the map of FIG. 4 and the map ofFIG. 6;

FIG. 8 is an output for the system that shows the output in the desiredterms and also compares a fixed value input with the results of anuncertain input; and

FIG. 9 discloses one of many possible tactical displays in which theoutput of the present invention may be used.

BRIEF DESCRIPTION OF THE PREFERRED EMBODIMENTS

Referring now to the drawings and more specifically to FIG. 1, there isshown an embodiment of an information processing system in accord withthe present invention. Fuzzy logic processing system 10 illustrates thatstimulus 11 is detected and its characteristics; are relayed to output36 using fuzzy logic when uncertainties related to stimulus 11 and/orother aspects of the input exist. Thus, the problem involvesuncertainties that propagate through system 10. Stimulus 11 may be oneor more physical phenomena of some type such as propeller rotationfrequency or pattern and may produce one or more of signals 12, 14 and16. One or more of signals 12, 14, and 16 are received by remote organicsensor 18, organic sensor 20, and/or off board sensor 22, respectively.Signals 12, 14 and 16 could be descriptive or related to a naturalphysical phenomena such as acoustic or electromagnetic waves interceptedby sonar or radar receivers. Remote organic sensor 18 and organic sensor20 use in this case the definition of organic as being sensorsassociated with the body or craft in that the sensors are eitherattached to craft or are remote but controlled by the craft. Organicsensors can therefore include a remotely launched probe, sonar, radar,or any type of device which detects physical phenomena such as, forinstance, detection of another object for tracking purposes. Off boardsensor 22 is independent of the body or craft and can transmit the datarelating to stimulus 11 which, as discussed above, may representnumerous different types of stimuli. The data collected by sensors 18,20 and/or 22 are transmitted through respective possible transmissionpaths 24, 26 and 28. These paths may have physical imperfections whichcan cause gaps in the data, as depicted by uncertainty 30. Uncertainty30 could arise in many ways and at many places in the processing systemand is pictured in a specific place only for convenience. For instance,sensors 18, 20 and 22 may not have detected all signals or have receivederroneous information, thus more uncertainty is present. There may be atime lag problem as discussed subsequently. It will be understood bythose skilled in the art that there are virtually an infinite number ofreasons that can arise to cause uncertainty within the input data. Thus,the result is that data with inherent uncertainties is received by fuzzyinference system 32 from one or more of signal paths 24, 26, and/or 28.Whiles prior art fuzzy inference systems cannot handle uncertain data,the inference system of the present invention can handle uncertain orcertain data, as explained in more detail hereinafter, using rules 34within fuzzy inference system 32 to yield output 36, that may be usedfor producing a tactical picture, guidance presets, motor control,decision support, and the like.

A presently preferred embodiment of fuzzy inference system 32 is shownin greater detail in FIG. 2. The collective data sent over paths 24, 26and/or 28 detected by sensors 18, 20 and 22 contains uncertainty asrepresented graphically by the region of fuzzy input 38 wherein it isuncertain what the precise value of fuzzy input 38 is. Fuzzy input 38 ismathematically represented by μ_(a) (x), with collective data thatvaries about the line x=a. Thus, whereas the prior art fuzzy inferencesystem would require a precise input, such as x=a, the system of thepresent invention can handle the uncertain input where it is known onlythat input x varies about a to form a set of possible values describedby the function μ_(a) (x). The region or set of values described byμ_(a) (x) is arbitrarily selected in the present example and could takeon many different forms or shapes. For the present explanation purposes,a Gaussian bell type curve distribution describes the region of possiblevalues for the input as shown by FIG. 5. The input could be in the formof a square wave, pulse, multiple sections, or other shapes. While theexample given herein uses a numerical characterization of uncertaindata, it will be understood that the present invention is not limited tonumerical characterization and could also be used with other types ofsymbolic characterizations of data as might be used for the particularproblem to be solved.

Fuzzy input 38 is received by fuzzy inference system 32 and operated onby extensor or cylindrical extensor 40. Cylindrical extensor 40, in thepresent example, operates on fuzzy input 38 to provide an extension offuzzy input 38 in the x, y plane to form extension 41, as represented by

 μ_(a)(x,y)=μ_(a)(x)∀y.  (1)

Extension 41 might be graphically described as adding an extra dimensionor, in this case, a third dimension. However, it will be understood thatdepending on the complexity of the problem many dimensions may beinvolved so that a visually understandable picture of an extended bellcurve as might be exemplified by FIG. 6 may not always be available forevery problem. The modified data or extension 41 is made available forfuzzy mapping section 42 for further operation. Fuzzy section 42 isderived from rules 34 and is mathematically represented by F(x,y) asmight be visualized in one example by FIG. 4 discussed below. Fuzzymapping section 42 operates on extension 41 to yet again amend the datato form the conditioned data or surface 43 or F_(c)(x,y) which might bevisualized in one example as shown in FIG. 7. The whole operationperformed by fuzzy mapping 42 can be described as

F_(c)(x,y)=min[F(x,y), μ(x,y)].  (2)

In the example of the present case, this can be verbally, symbolically,or linguistically restated as the graphical intersections of rules 34and the output of cylindrical extensor 40 to form conditioned surface43. Now retro-projector 44 receives the F_(c)(x, y) or conditionedsurface 43 and transforms it into fuzzy output 46 or M_(μa)(y) as mightbe graphically represented as shown in FIG. 8. This process can besymbolized by the formula

M_(μa)(y)=max_(?x)[F_(c)(x,y)].  (3)

The process of the above equation may be visually or graphicallydescribed as projecting the data output from fuzzy mapping 42 onto the yplane, resulting again in two dimensional data having the desired outputunits as shown, for example in FIG. 8.

FIG. 3 provides an example that Graphically depicts the operation offuzzy inference system 32 in a particular case as discussed below. Point48 represents the value x=a, an exact value. While a prior art fuzzylogic inference system would require such an exact value, a crisp inputvalue is not known in the present example so shaded region 52generalizes x as being in that area, defined by the function μ_(a) (x).Had x=a been known, point 50 would represent y=b, an exact value asmight be described using a prior art fuzzy logic inference system. Thecylindrical extensor extends the function μ_(a) (X) into anotherdimension with

μ_(a)(x,y)=μ_(a)(x)∀y.  (4)

as discussed above. The input data now is uncertain but the set ofpossible values has the form μ_(a) (X,y) represented by the shaded areabetween lines 49 and C1. The rules are graphically described as thevalue between lines 54 and 56 and represented by F(x,y). Shaded region58 is generated by the cylindrical extensor output and the rules to formthe conditioned surface, represented by F_(c) (x,y). In other words, thearea defined by lines 49, 51, 54 and 56. Mathematically, F_(c)(x,y)=F(x,y) ?μ)(x,y), with ? being a fuzzy composition operation. Incomparison, had exact values been known as required in a prior art fuzzylogic inference system, line 60 would accurately depict this output.Finally, the data is transformed back into usable form by projecting thegraphical image into the desired units by removing a dimension,represented by line 62. This is the fuzzy output M_(μa) (y), given by

M_(μa)(y)=max_(?x)[F_(c)(x,y)].  (5)

FIGS. 4-9 give a possible example of a step-by-step graphicalrepresentation of the process of fuzzy inference system 32 using morespecifically an example of tracking a platform such as a submarine orship in relation to another platform. It will be understood that this isone example only given for explanatory purposes only. The invention isnot intended to be limited by this example and may be used in differentapplications and conditions, including, but not limited to: medical,industrial, marine, warfare, exploration, and numerous other settings.

Referring now to FIG. 4 and subsequent figures, the axes x, y and z aredefined as class axis 66, membership axis 68 and speed axis 74,respectively. The example is based upon a two rule, one input, oneoutput fuzzy inference system, which will provide target speed basedupon detected class. The target will have a higher speed if it has anuclear engine than if it has a diesel engine. Class axis 66 ranges fromzero to one, with zero denoting diesel and one representing nuclear.Membership axis 68 also ranges from zero to one, with zero designatingno membership and one indicating one hundred percent membership in thespecified class. Therefore, a target is diesel if class equals zero andmembership equals one, whereas a target is nuclear if class equals oneand membership equals one. Speed axis 74 ranges from zero to forty, orthe range of speed of the target in knots. A nuclear engine target ismore likely to be traveling in the range of 15 to 30 knots as comparedwith a diesel engine target with a speed more likely in the range of 5to 15 knots. The rules can be summed up as the following: (1) if classis diesel, then speed is low and (2) if class is nuclear then speed ishigh, as depicted by the three dimensional rule graph 70 represented bythe function F(x,y). The rules for the present example are graphicallydisplayed as a set, map, figure, or curve as shown in FIG. 4. It will beunderstood that while the map or set of values indicated may be easilyvisualized in three dimensions as in the example of FIG. 4, othersystems of rules, inputs, and/or outputs may use many dimensional orn-dimensional maps that are not so easily visualized.

The example further continues with the fact that sensors and/or otherintelligence have detected a target with class equal to about 0.3 withsome amount of uncertainty. The sensors used for this purpose mightconceivably be sonar receivers that detect a propeller speed or type ofsound or the like. The result of the detected input is graphicallydepicted in FIG. 5 with an input that is uncertain but has possiblevalues about point 64, representing x=0.3. For this example, a Gaussianmembership function, graph, map or set 72 describes the fuzzy input. Thetarget is not described exactly in class equal to 0.3, but rather isdescribed as a possible value within the area under the curve butuncertain as to exactly what that value is. If the input were definite,such that the information was exactly 0.3, then the present inventionwould also operate to handle that situation in the same manner asdiscussed below. Function 72 would then simply be a straight line at0.3. Thus, the present invention is operable with both definite anduncertain data. However assuming the input to be uncertain, thenGaussian membership function 72, for an example only, may have formulaequal to $\begin{matrix}{{\mu_{0.3}(x)} = {\exp \left\lbrack {- \frac{\left( {x - 0.3} \right)^{2}}{2*0.05^{2}}} \right\rbrack}} & (6)\end{matrix}$

In accord with the system and method of the present invention, Gaussianmembership function 72 is extended onto speed axis 74 to makecylindrical extension map, function, set or graph 76 as shown in FIG. 6,depicting the operation of extensor or cylindrical extensor 40. Thus,the input is extended onto at least one dimension characteristic of therules. Cylindrical extension map, set, function or graph 76, representedby μ_(0.3) (x,y), is obtained by the mathematical expression discussedabove, i.e.,

μ_(a)(x,y)=μ_(a)(x)∀y.  (7)

Fuzzy mapping section 42 operates on cylindrical extension map, set, orgraph 76 and rule map, set, or graph 70. In the present example anintersection is found as the result of this operation that provides map,set, or conditioned surface graph 78 as shown in FIG. 7. Conditionedsurface map, set or graph 78, noted as F_(c) (x,y), was obtained inaccord with the equation expressed above, i.e.,

F_(c)(x,y)=min[F(x,y), μ(x,y)].  (8)

In FIG. 8, conditioned surface graph 78 is fed into the retro-projectorwhere the graph is projected onto speed axis 74 and possibility axis 84.A dimension is reduced in accord with the desired output terms resultingin two dimensional output fuzzy data 80. Interpretation of this graphstates that the target is more likely to be traveling about ten knots,but still has the possibility of going about twenty to thirty knots,though this is not as likely. Line 82 is the graph obtained had exactvalues been taken with x=0.3, resulting in the uncertainty obtainedusing fuzzy input being deleted and never taken into account. Had thisbeen done as would have been a plausible solution for prior art fuzzylogic inference systems that require an exact input, then the likelihoodwould have been significantly greater that the speed in the range ofabout ten knots. Thus, a decision maker might have been more likely tomake a decision that committed too early a decision based on the targetspeed being in the range of about ten knots. A better decision wouldhave been to wait as long as possible before committing due to theincreased possibility that the speed was in the range of from fifteen tothirty knots when the uncertainty of the input was included in thecalculation.

In a bearings-only target motion analysis problem it is necessary toestimate contact location and motion parameters using a time series ofbearing measurements. A fundamental problem of the bearings-only targetmotion analysis application is that the contact range is not observablefor a single-leg of ownship motion wherein a leg is defined as a timeinterval of a constant platform velocity, such as that of a ship orsubmarine. A time lag is therefore introduced into the estimationprocess owing to the necessity of collecting sufficient data on all legsof motion. In some cases rapid estimates are needed even though they maybe of poorer quality due to the time urgency of the tactical situation.Tactical map 86, shown in FIG. 9, is one possible resulting use of thedata obtain about the target. The fuzzy characterization of contactspeed as discussed above using the fuzzy logic inference system outputas shown in FIG. 8 is used to produce an enhanced area of uncertaintydescription for the single leg target motion analysis. Tactical map 86shows in Kyards the target's suspected contact end-point position 96 inrelation to observer 94. Tactical map 86 also displays area 88, depicted50% possibility of the target end point being in that area. Area 90depicts 85% confidence, and area 92 denotes 98% confidence of the targetbeing within those borders. For reference, location 98 is the actualtarget end point position. While end-points of the likely tracks aredisplayed in tactical map 86, the various possible tracks with the mostlikely track may also be viewed with a colored intensity weighting forthe likelihood of the various tracks.

In summary, the present inventional is operable for using uncertain dataas described as an example only in FIG. 5. This data is operated on toproduce data with one or more dimensions added as shown in this examplein FIG. 6. The rules are graphically displayed in this example in FIG.4. The result of operating on the extended data with the rules is shownin FIG. 7. This data is then projected onto the units desired for outputas shown in FIG. 8. The resulting output may then be used in a controlsystem as desired and can be seen in the tactical display of FIG. 9.

It will be understood that many additional changes in the details,materials, steps and arrangement of parts, which have been hereindescribed and illustrated in order to explain the nature of theinvention, may be made by those skilled in the art within the principleand scope of the invention as expressed in the appended claims.

What is claimed is:
 1. A fuzzy logic system for processing informationfrom at least one input that is associated with one or more physicalphenomena, said at least one input being described as an input setincluding a plurality of possible values with uncertainty as to which ofsaid plurality of possible values is the actual value of said at leastone input, said fuzzy logic system comprising: an extensor for operatingon said input set to produce an operated set of values; an inferenceengine that includes a plurality of rules related to said one or morephysical phenomena, said inference engine being operable for comparingsaid operated set of values with said plurality of rules for producing aconditioned set of values; and a projector for producing an outputsignal from said conditioned set of values.
 2. The system of claim 1wherein said output signal is used for producing a display.
 3. Thesystem of claim 1 wherein said output signal is used as a controlsignal.
 4. The system of claim 1 wherein said operated set operated setof values may be represented by a first map and said rules may berepresented by a second map.
 5. The system of claim 4 wherein saidconditioned set may be represented as a third map that is anintersection of said first map and said second map.
 6. The system ofclaim 5 wherein said projector produces a signal that may be representedas a conditioning of said third map with respect to desired outputunits.
 7. A method for a control system for utilizing uncertain inputdata that has an uncertain value contained within an input set ofpossible values, said uncertain input data being associated with one ormore physical phenomena, said method comprising the steps of:representing said uncertain input data for use in a fuzzy logic system,said representation describing said input set of possible values withoutdesignating a specific of said possible values for said uncertain inputdata; providing a plurality of rules fi r said fuzzy logic system interms related to said uncertain input data; and inferring an output fromsaid fuzzy logic system based on said plurality of rules and said set ofpossible values for said input data.
 8. The method of claim 7 furthercomprising operating on said input set of possible values to produce afirst set of possible values.
 9. The method of claim 8 furthercomprising representing said rules for said fuzzy logic system in termsthat may be described by a second set of values.
 10. The method of claim9 further comprising producing a third set of values from said first setof values and said second set of values.
 11. The method of claim 10wherein said third set comprises an intersection of said first set andsaid second set.
 12. The method of claim 7 further comprising: producinga tactical picture that incorporates said output from said fuzzy logicsystem.
 13. The method of claim 7, further comprising: providing asystem control signal from said output of said fuzzy logic system.
 14. Acontrol system, comprising: at least one sensor for producing a sensorsignal, said sensor signal being used to produce input information forsaid control system; and a fuzzy logic inference system for said controlsystem, said fuzzy logic inference system being operable for receivingsaid input information when said input information is definite and alsowhen said input information is uncertain such that a representation ofsaid information is produced when said input information is definite andalso when said information is uncertain, when said input information isuncertain said representation provides for a set of possible values forsaid input information, said fuzzy logic inference system being operablefor comparing said representation to a set of rules to produce an outputcontrol signal.
 15. The control system of claim 14 wherein said outputcontrol signal is used to create a tactical picture display.
 16. Thecontrol system of claim 14 wherein said fuzzy logic inference systemfurther comprises: an extensor for operating on said input informationto produce a first map by introducing at least one additional dimensionto said input information, said at least one additional dimension beingrelated to said set of rules.
 17. The control system of claim 16 whereinsaid rules are represented by a second map that includes said at leastone additional dimension.
 18. The control system of claim 17 whereinsaid fuzzy logic inference system is operable for comparing said firstmap to said second map to produce a third map.
 19. The control system ofclaim 18 wherein said fuzzy logic inference system further comprises: aprojector for operating on said third map to form a projection onto adimension for said output control signal.
 20. The control system ofclaim 14 further comprising: a decision support system wherein saidoutput control signal is used to provide a display depicting range ofpossibilities.